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首页> 外文期刊>Proceedings of the International Association of Hydrological Sciences >Numerical simulation of land subsidence above an off-shore Adriatic hydrocarbon reservoir, Italy, by Data Assimilation techniques
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Numerical simulation of land subsidence above an off-shore Adriatic hydrocarbon reservoir, Italy, by Data Assimilation techniques

机译：近岸亚得里亚碳氢化合物库，意大利土地沉降的数值模拟，通过数据同化技术

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摘要

The numerical prediction of land subsidence above producing reservoirs can be affected by a number of uncertainties, related for instance to the deep rock constitutive behavior, geomechanical properties, boundary and forcing conditions, etc. The quality and the amount of the available observations can help reduce such uncertainties by constraining the numerical model outcome and providing more reliable estimates of the unknown governing parameters. In this work, we address the numerical simulation of land subsidence above a producing hydrocarbon field in the Northern Adriatic, Italy, by integrating the available monitoring data in the computational model with the aid of Data Assimilation strategies. A preliminary model diagnostic analysis, i.e. the χ2-test, allows for identifying the most appropriate forecast ensemble. Then, a Bayesian approach, i.e. the Red Flag technique, and a smoother formulation, i.e. the Ensemble Smoother, provide a significant reduction of the prior uncertainties. The experiment developed on a real-world gas field confirms that the integration of monitoring observations with classical geomechanical models is a valuable approach to improve the reliability of land subsidence predictions and to exploit in a systematic way the increasing amount of available measurement records.
机译：上面的地面沉降的数值预测可以受到许多不确定性的影响，例如，与深层岩石组成型行为，地质力学性质，边界和强迫条件等相关的影响。质量和可用观察的量可以帮助减少通过约束数值模型结果并提供更可靠的控制管理参数的可靠估计的这种不确定性。在这项工作中，我们通过在数据同化策略的帮助下将可用的监测数据集成在计算模型中，解决了意大利北部亚得里亚特的生产碳氢化合物场的数值模拟。初步模型诊断分析，即χ2-test，允许识别最合适的预测集合。然后，贝叶斯方法，即红旗技术，以及更平滑的配方，即集合更顺畅，提供了预先不确定性的显着降低。在现实世界的天然气领域开发的实验证实，通过古典地质力学模型的监测观测的整合是一种有价值的方法，可以提高土地沉降预测的可靠性，并以系统的方式利用越来越多的可用测量记录。

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